Remark
Please be aware that these lecture notes are accessible online in an ‘early access’ format. They are actively being developed, and certain sections will be further enriched to provide a comprehensive understanding of the subject matter.
3.4. Attribute Tables#
In Geographic Information Systems (GIS), attribute tables are essential components that store non-spatial data linked to spatial features. Each spatial feature on a map, such as a building, road, or land parcel, corresponds to a record in the attribute table. This record is connected to the feature through a unique numerical identifier known as a Feature Identifier (FID). For example, a park (spatial feature) on a GIS map may have an FID of 102, and its corresponding record in the attribute table could include attributes like area, vegetation type, and usage regulations.
Example: Let’s take a look at the attribute tables for the dataset from the Calgary Community Boundaries. The example shows a snippet of an attribute table for the Calgary Community Boundaries dataset. It illustrates how each spatial feature, like a park or residential area, is associated with a record in the table, identified by a unique Feature Identifier (FID). The table includes various attributes such as class, class code, community code, name, sector, and more, which describe the non-spatial characteristics of the spatial features. The purpose is to show how GIS integrates spatial data (like MULTIPOLYGON geometries) with descriptive information, enabling detailed analysis and decision-making. It highlights the importance of attribute tables in managing and utilizing geospatial data effectively.
| CLASS | CLASS_CODE | COMM_CODE | NAME | SECTOR | SRG | COMM_STRUCTURE | CREATED_DT | MODIFIED_DT | MULTIPOLYGON | geometry |
|---|---|---|---|---|---|---|---|---|---|---|
|
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3.4.1. Raster Data Attributes#
Raster data play a crucial role in Geographic Information Systems (GIS), where they serve as a fundamental means of representing spatial information through the values assigned to each pixel. These pixels are not just mere placeholders of spatial data; they can be categorized using unique integer values, which allows them to be linked to a set of attributes. This categorization is especially significant in land cover datasets, where different environmental features such as water bodies, forests, and urban areas are denoted by these pixel values. Each category is meticulously described in an attribute table, which includes detailed characteristics like the quality of water, the density of forests, or the regulations governing urban zones.
Example - Visualizing Raster Data with Heatmaps: To illustrate this concept, imagine a 10x10 matrix that represents a raster. This hypothetical raster data can be depicted through a heatmap, where a colorbar indicates the value of each cell by assigning specific colors. This technique is commonly applied in various types of raster data visualization, such as land surface temperature, elevation, and more, to effectively convey differences in values.
Fig. 3.5 Visualizing Raster Data with a Heatmap. This image depicts a 10x10 heatmap representing a raster dataset. Each cell in the matrix is assigned a color based on its value, as indicated by the colorbar on the right. The colorbar ranges from 50 (light yellow) to 250 (dark red), effectively illustrating variations in the data.#
While the heatmap offers a clear visualization of the data, assigning discrete values to each pixel that could represent different land cover categories, it’s important to remember that not all raster data formats are compatible with attribute tables. In many GIS applications, raster data are utilized without the accompaniment of attribute tables, relying solely on the inherent pixel values to convey the necessary spatial information. This approach underscores the versatility and adaptability of raster data in various GIS applications, despite the potential limitations posed by the absence of attribute tables in certain data formats.
Example:
To demonstrate a practical application of raster data visualization, let’s take the MODIS Land Cover Type Product (MCD12Q1 - v061) as an example. By employing Python and Geemap, we can generate a map and overlay a layer to display the land cover data. This is achieved using a predefined color palette that aligns with the International Geosphere-Biosphere Programme (IGBP) land cover classification system.
Fig. 3.6 provides a visual representation of land cover types in the area surrounding Calgary, Alberta, Canada, based on the MODIS Land Cover Type data for the year 2013. The map is color-coded according to the International Geosphere-Biosphere Programme (IGBP) land cover classification scheme, which includes various categories such as forests, shrublands, wetlands, and urban areas. Each category is assigned a specific color, making it easy to identify different land cover types at a glance. The map also features a legend that correlates the colors with the land cover categories, aiding in interpretation. This visualization serves as a valuable tool for understanding the distribution and extent of different ecosystems and land uses in the region.
Fig. 3.6 This map illustrates the diverse land cover types in the vicinity of Calgary, using the MODIS Land Cover Type data from 2013. The color-coded representation reflects the IGBP classification, providing insights into the region’s ecological diversity, from urban areas to natural vegetation and water bodies.#
3.4.2. Measurement Levels#
Attributes in GIS are categorized into four measurement levels, each with distinct characteristics:
Nominal Data: These are categorical data without any numeric significance or order. For example, land use types such as residential, commercial, and industrial are nominal data.
Ordinal Data: This data type has a ranked order but no fixed interval between ranks. A soil erosion risk map might classify areas as low, moderate, or high risk, which are ordinal data.
Interval Data: Numeric data with equal intervals but no true zero point. Temperature scales like Celsius and Fahrenheit are interval data because the difference between degrees is the same, but there is no absolute zero.
Ratio Data: Similar to interval data but with a meaningful zero point, allowing for the comparison of relative magnitudes. Examples include population counts and annual rainfall measurements, where zero represents none or no occurrence.